Is inference a part of the model training rather than prediction?
In the field of machine learning, specifically in the context of Google Cloud Machine Learning, the statement "Inference is a part of the model training rather than prediction" is not entirely accurate. Inference and prediction are distinct stages in the machine learning pipeline, each serving a different purpose and occurring at different points in the
What does serving a model mean?
Serving a model in the context of Artificial Intelligence (AI) refers to the process of making a trained model available for making predictions or performing other tasks in a production environment. It involves deploying the model to a server or cloud infrastructure where it can receive input data, process it, and generate the desired output.
Why is it important for TFX to keep execution records for every component each time it is run?
It is important for TFX (TensorFlow Extended) to maintain execution records for every component each time it is run due to several reasons. These records, also known as metadata, serve as a valuable source of information for various purposes, including debugging, reproducibility, auditing, and model performance analysis. By capturing and storing detailed information about the
- Published in Artificial Intelligence, EITC/AI/TFF TensorFlow Fundamentals, TensorFlow Extended (TFX), Metadata, Examination review
What are the horizontal layers included in TFX for pipeline management and optimization?
TFX, which stands for TensorFlow Extended, is a comprehensive end-to-end platform for building production-ready machine learning pipelines. It provides a set of tools and components that facilitate the development and deployment of scalable and reliable machine learning systems. TFX is designed to address the challenges of managing and optimizing machine learning pipelines, enabling data scientists